A Component-Based Localization Algorithm for Sparse 3-D Wireless Sensor Networks

Node localization is one of the most essential features of wireless sensor networks (WSNs). Vavarious localization algorithms exist for densely deployed 3-D wireless sensor networks. However, for a sparse 3-D network, range-based localization is still a challenging task because it is difficult to fi...

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Bibliographic Details
Published in:IEEE access Vol. 12; pp. 51904 - 51918
Main Authors: Islam, Mazhar, Ikram, Muhammad, Alhussein, Musaed, Ayub, Muhammad Sohaib, Khan, Muhammad Asad, Aurangzeb, Khursheed
Format: Journal Article
Language:English
Published: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
3-D localization
Algorithm design and analysis
Algorithms
component-based localization
Hardware
Internet of Things
Localization
Localization method
Location awareness
Merging
Nodes
patch and stitching localization
Sensors
Task analysis
Three-dimensional displays
Wireless communication
Wireless sensor network
Wireless sensor networks
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Node localization is one of the most essential features of wireless sensor networks (WSNs). Vavarious localization algorithms exist for densely deployed 3-D wireless sensor networks. However, for a sparse 3-D network, range-based localization is still a challenging task because it is difficult to find sufficient anchor nodes and distance information among nodes in a sparse 3-D network. To mitigate the sparseness issues in 3-D sensor networks, we present a component-based localization method in this paper in which we split the entire network into small overlapping sub-networks called components and assign local coordinates to each component. Then, we merge these small components to make a globally coordinated system. With a meager anchor ratio, we localize the whole network. We define merging conditions according to the number of common nodes, actual measured distances among nodes, and the calculated distance based on the local coordinates of the nodes. We assess how well our proposed algorithm performs by conducting extensive simulations. The outcomes confirm that the proposed algorithm works comparatively better in a sparse 3-D sensor network than in a densely deployed 3-D sensor network. Our algorithm localizes more than 83% of nodes at a node degree of 10 having 5% anchor ratio; however, other algorithms localize only 18%-79% in the same scenario.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3358889